Append Pandas Series to DataFrame as a column [duplicate]

Question

I have panadas dataframe (df) like ['key','col1','col2','col3'] and I have pandas series (sr) for which the index is the same as 'key' in data frame. I want to append the series to the dataframe at the new column called col4 with the same 'key'. I have the following code:

for index, row in segmention.iterrows():    
     df[df['key']==row['key']]['col4']=sr.loc[row['key']]

The code is very slow. I assume there should be more efficient and better way to do that. could you please help?

Answer 1

You can simply do:

df['col4'] = sr 

If don't misunderstand.

Answer 2

Use map as mentioned EdChum:

df['col4'] = df['key'].map(sr)
print (df)
   col1  col2  col3 key  col4
0     4     7     1   A     2
1     5     8     3   B     4
2     6     9     5   C     1

Or assign with set_index:

df = df.set_index('key')
df['col4'] = sr
print (df)
     col1  col2  col3  col4
key                        
A       4     7     1     2
B       5     8     3     4
C       6     9     5     1

If dont need align data in Series by key use (see difference 2,1,4 vs 4,1,2):

df['col4'] = sr.values
print (df)
   col1  col2  col3 key  col4
0     4     7     1   A     4
1     5     8     3   B     1
2     6     9     5   C     2

Sample:

df = pd.DataFrame({'key':[1,2,3],
                   'col1':[4,5,6],
                   'col2':[7,8,9],
                   'col3':[1,3,5]}, index=list('ABC'))

print (df)
   col1  col2  col3  key
A     4     7     1    1
B     5     8     3    2
C     6     9     5    3

sr = pd.Series([4,1,2], index=list('BCA'))
print (sr)
B    4
C    1
A    2
dtype: int64

df['col4'] = df['key'].map(sr)
print (df)
   col1  col2  col3 key  col4
0     4     7     1   A     2
1     5     8     3   B     4
2     6     9     5   C     1

---

df = df.set_index('key')
df['col4'] = sr
print (df)
     col1  col2  col3  col4
key                        
A       4     7     1     2
B       5     8     3     4
C       6     9     5     1